Improving the performance of the MM/PBSA and MM/GBSA methods in recognizing the native structure of the Bcl-2 family using the interaction entropy method

2020 ◽  
Vol 22 (7) ◽  
pp. 4240-4251 ◽  
Author(s):  
Susu Zhong ◽  
Kaifang Huang ◽  
Song Luo ◽  
Shuheng Dong ◽  
Lili Duan
Keyword(s):  
Drug Design ◽  
Entropy Method ◽  
Native Structure ◽  
Correct Discrimination

Correct discrimination of native structure plays an important role in drug design. IE method significantly improves the performance of MM/PB(GB)SA method in discriminating native and decoy structures in protein–ligand/protein systems of Bcl-2 family.

scholarly journals Localization of ligands within human carbonic anhydrase II using 19F pseudocontact shift analysis

2019 ◽  
Vol 10 (19) ◽  
pp. 5064-5072 ◽  
Author(s):  
Kaspar Zimmermann ◽  
Daniel Joss ◽  
Thomas Müntener ◽  
Elisa S. Nogueira ◽  
Marc Schäfer ◽  
...  
Keyword(s):  
Drug Design ◽  
Carbonic Anhydrase ◽  
Rational Drug Design ◽  
Carbonic Anhydrase Ii ◽  
Pseudocontact Shift ◽  
Native Structure ◽  
Shift Analysis ◽  
Rational Drug ◽  
Human Carbonic Anhydrase

Unraveling the native structure of protein–ligand complexes in solution enables rational drug design.

The native structure of the eukaryotic ribosome

1983 ◽  
Vol 41 ◽  
pp. 432-433
Author(s):  
R.A. Milligan ◽  
P.N.T. Unwin
Keyword(s):  
Ribosomal Proteins ◽  
Crystal Form ◽  
Native Structure ◽  
X Ray Diffraction ◽  
Eukaryotic Ribosome ◽  
Vitro Analysis ◽  
In Vitro Analysis ◽  
Resolution Structure ◽  
Stable Unit

A detailed understanding of the mechanism of protein synthesis will ultimately depend on knowledge of the native structure of the ribosome. Towards this end we have investigated the low resolution structure of the eukaryotic ribosome embedded in frozen buffer, making use of a system in which the ribosomes crystallize naturally.The ribosomes in the cells of early chicken embryos form crystalline arrays when the embryos are cooled at 4°C. We have developed methods to isolate the stable unit of these arrays, the ribosome tetramer, and have determined conditions for the growth of two-dimensional crystals in vitro, Analysis of the proteins in the crystals by 2-D gel electrophoresis demonstrates the presence of all ribosomal proteins normally found in polysomes. There are in addition, four proteins which may facilitate crystallization. The crystals are built from two oppositely facing P4 layers and the predominant crystal form, accounting for >80% of the crystals, has the tetragonal space group P4212, X-ray diffraction of crystal pellets demonstrates that crystalline order extends to ~ 60Å.

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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